Tumour microenvironment (TME) of breast cancer mainly comprises malignant, stromal, immune, and tumour infiltrating lymphocyte (TILs). Assessment of TILs is crucial for determining the disease’s prognosis. Manual TIL assessments are hampered by multiple limitations, including low precision, poor inter-observer reproducibility, and time consumption. In response to these challenges, automated scoring emerges as a promising approach. The aim of this systematic review is to assess the evidence on the approaches and performance of automated scoring methods for TILs assessment in breast cancer. This review presents a comprehensive compilation of studies related to automated scoring of TILs, sourced from four databases (Web of Science, Scopus, Science Direct, and PubMed), employing three primary keywords (artificial intelligence, breast cancer, and tumor-infiltrating lymphocytes). The PICOS framework was employed for study eligibility, and reporting adhered to the PRISMA guidelines. The initial search yielded a total of 1910 articles. Following screening and examination, 27 studies met the inclusion criteria and data were extracted for the review. The findings indicate a concentration of studies on automated TILs assessment in developed countries, specifically the United States and the United Kingdom. From the analysis, a combination of sematic segmentation and object detection (n = 10, 37%) and convolutional neural network (CNN) (n = 11, 41%), become the most frequent automated task and ML approaches applied for model development respectively. All models developed their own ground truth datasets for training and validation, and 59% of the studies assessed the prognostic value of TILs. In conclusion, this analysis contends that automated scoring methods for TILs assessment of breast cancer show significant promise for commodification and application within clinical settings.

Background: Breast cancer is one of the leading causes of death among women worldwide. Accurate early detection of lymphocytes and molecular biomarkers is essential for improving diagnostic precision and patient prognosis. Whole slide images (WSIs) are central to digital pathology workflows in breast cancer assessment. However, applying deep learning techniques to WSIs presents persistent challenges, including variability in image quality, limited availability of high-quality annotations, poor model interpretability, high computational demands, and suboptimal processing efficiency. Methods: This systematic review, guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), examines deep learning-based detection methods for breast cancer published between 2020 and 2024. The analysis includes 39 peer-reviewed studies and 20 widely used WSI datasets. Results: To enhance clinical relevance and guide model development, this study introduces a five-dimensional evaluation framework covering accuracy and performance, robustness and generalization, interpretability, computational efficiency, and annotation quality. The framework facilitates a balanced and clinically aligned assessment of both established methods and recent innovations. Conclusions: This review offers a comprehensive analysis and proposes a practical roadmap for addressing core challenges in WSI-based breast cancer detection. It fills a critical gap in the literature and provides actionable guidance for researchers, clinicians, and developers seeking to optimize and translate WSI-based technologies into clinical workflows for comprehensive breast cancer assessment.

Background: Tumour-infiltrating lymphocytes (TILs) are emerging as a crucial prog-nostic biomarker in triple-negative breast cancer (TNBC). However, their clinical utility remains constrained by the subjectivity and interobserver variability of manual scoring, despite standardization efforts by the International TILs Working Group (TIL-WG). This study aimed to evaluate the interobserver agreement among pathologists in scoring stromal and intratumoral TILs from H&E-stained TNBC slides and to identify contrib-uting histological factors. Methods: Two consultant pathologists at Hospital Canselor Tuanku Muhriz, Kuala Lumpur independently assessed 64 TNBC cases using TIL-WG guidelines. Interobserver agreement was quantified using intraclass correlation coeffi-cient (ICC) and Cohen's Kappa coefficient. Cases with over 10% scoring discrepancies underwent review by a third pathologist and a consensus discussion was held to explore the underlying confounders. Results: Our results showed moderate interobserver agreement for stromal TILs (ICC = 0.58) and strong agreement for intratumoral TILs (ICC = 0.71). Significant variability was attributed to three main confounding variables: heterogeneous TIL distribution, poorly-defined tumor-stroma interface and focal dense lymphoid infiltrates. Conclusions: These findings underscore the critical need for standardized TILs assessment criteria advocating for the adoption of AI-based scoring method to overcome interobserver variability and enhance the reproducibility of eval-uations.